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      <a href="/2020/08/05/Jetson-TX2-%E9%85%8D%E7%BD%AE%E6%B5%81%E7%A8%8B/">
        <p class="h4 index-header">Jetson TX2 配置流程</p>
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          <div class="index-text mb-1">Jetson TX2 配置流程
刷机


下载sdkmanager
https://developer.nvidia.com/embedded/jetpack

Flash OS结束后，配置用户名和密码，修改APT源
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restrict</div>
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          <i class="iconfont icon-riqi2"></i>&nbsp;2020-08-05&nbsp;&nbsp;
        
        
        
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            <a href="/tags/Jetson">Jetson</a>&nbsp;
          
        
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      <a href="/2020/04/26/YOLOV4/">
        <p class="h4 index-header">YOLOV4 原理</p>
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      <a href="/2020/04/26/YOLOV4/">
        
        
          
        
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          <div class="index-text mb-1">YOLOv4: Optimal Speed and Accuracy of Object Detection表现
贡献论文提出的YOLOV4，从表现看比起YOLOV3，AP和FPS分别提升了10%，12%，论文的主要贡献：

提出速度更快、精度更好的检测模型，仅需要单张1080Ti或2080Ti即可完成训练。
验证了目前SOTA的Bag of Freebies(不增加推理成本的trick)和Bag of Specials(增加推理成本的trick)的有效性。
修改了SOTA方法，让其更高效且更合适地在单卡进行训练，包括CBN、PAN、SAM等。

YOLOV4结构
Backbone: CSPD</div>
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          <i class="iconfont icon-riqi2"></i>&nbsp;2020-04-26&nbsp;&nbsp;
        
        
        
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            <a href="/tags/YOLOV4">YOLOV4</a>&nbsp;
          
        
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      <a href="/2020/04/06/FPGA-FSM/">
        <p class="h4 index-header">FPGA FSM</p>
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      <a href="/2020/04/06/FPGA-FSM/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">FPGA  有限状态机 三段式写法检测“HELLO”字符串流，状态机描述如下：

module state(Clk, Rst_n, data, out);
    input Clk;
    input Rst_n;
    input [7:0]data;
    output reg out;

    localparam
        CHECK_H    = 6&#39;b000001,
        CHECK_E    = 6&#39;b000010,
        CHECK_L1   = 6&#39;b000100,
        CHECK_L2   = 6&#3</div>
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          <i class="iconfont icon-riqi2"></i>&nbsp;2020-04-06&nbsp;&nbsp;
        
        
        
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            <a href="/tags/FPGA">FPGA</a>&nbsp;
          
        
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      <a href="/2020/03/12/detect/">
        <p class="h4 index-header">深入理解目标检测</p>
      </a>

      <a href="/2020/03/12/detect/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">对计算机而言，能够“看到”的是图像被编码之后的数字，但它很难解高层语义概念，比如图像或者视频帧中出现目标的是人还是物体，更无法定位目标出现在图像中哪个区域。目标检测的主要目的是让计算机可以自动识别图片或者视频帧中所有目标的类别，并在该目标周围绘制边界框，标示出每个目标的位置，如 图1 所示。

图4：边界框
通常有两种格式来表示边界框的位置：

xyxy，即$(x_1, y_1, x_2, y_2)$，其中$(x_1, y_1)$是矩形框左上角的坐标，$(x_2, y_2)$是矩形框右下角的坐标。图4中3个红色矩形框用xyxy格式表示如下：


左：$(40.93, 141.1, 226.99</div>
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          <i class="iconfont icon-riqi2"></i>&nbsp;2020-03-12&nbsp;&nbsp;
        
        
        
          <i class="iconfont icon-tag"></i>
          
            <a href="/tags/%E7%9B%AE%E6%A0%87%E6%A3%80%E6%B5%8B,%20YOLOV3">目标检测, YOLOV3</a>&nbsp;
          
        
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      <a href="/2020/03/07/YOLOV3/">
        <p class="h4 index-header">YOLOV3 原理</p>
      </a>

      <a href="/2020/03/07/YOLOV3/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">YOLOV3 原理
YOLOV3  网络结构



特征图C与预测框P的关系以C0特征层输出为例，C0的shape为[1024, 13, 13], 经过YOLO Detection Layer 的多次卷积，特征层叠加最后输出P0[36, 13, 13]。其中36=3*(5+7)，对应关系如下图所示：



​        如上图所示，输出的P0一个像素共有3（5+C）个预测框，其中P0的一个像素就对应着将图片划成13  13个格子中的一个格子。其中的5表示[tx, ty, th, tw, objectness], 输出的预测坐标格式为(tx, ty, th, tw),需要转化成真实（x1, </div>
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      <div class="index-btm">
        
          <i class="iconfont icon-riqi2"></i>&nbsp;2020-03-07&nbsp;&nbsp;
        
        
        
          <i class="iconfont icon-tag"></i>
          
            <a href="/tags/YOLOV3">YOLOV3</a>&nbsp;
          
        
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      <a href="/2020/03/07/Conv/">
        <p class="h4 index-header">卷积神经网络相关计算</p>
      </a>

      <a href="/2020/03/07/Conv/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">卷积神经网络相关计算
单通道图片输入


输入shape: [$H{in}$, $W{in}$], 卷积核shape: [$k_h$,$k_w$],  padding: $p_h$, $p_w$,  stride: $s_h$, $s_w$
输出特征图尺寸：

H_{out} = \frac{H_{in} + 2p_h - k_h}{s_h} + 1
W_{out} = \frac{W_{in} + 2p_w - k_w}{s_w} + 1
多通道图片输入单通道输出



输入shape:[C_{in}, H_{in}, W_{in}], 卷积核shape:[C_{in},k_h,k_w]输出</div>
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          <i class="iconfont icon-riqi2"></i>&nbsp;2020-03-07&nbsp;&nbsp;
        
        
        
          <i class="iconfont icon-tag"></i>
          
            <a href="/tags/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">卷积神经网络</a>&nbsp;
          
        
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      <a href="/2020/03/07/ConvNet/">
        <p class="h4 index-header">卷积神经网络</p>
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      <a href="/2020/03/07/ConvNet/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">（文章来源：Baidu AI Studio）计算机视觉作为一门让机器学会如何去“看”的科学学科，具体的说，就是让机器去识别摄像机拍摄的图片或视频中的物体，检测出物体所在的位置，并对目标物体进行跟踪，从而理解并描述出图片或视频里的场景和故事，以此来模拟人脑视觉系统。因此，计算机视觉也通常被叫做机器视觉，其目的是建立能够从图像或者视频中“感知”信息的人工系统。
计算机视觉技术经过几十年的发展，已经在交通（车牌识别、道路违章抓拍）、安防（人脸闸机、小区监控）、金融（刷脸支付、柜台的自动票据识别）、医疗（医疗影像诊断）、工业生产（产品缺陷自动检测）等多个领域应用，影响或正在改变人们的日常生活和工业生产</div>
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      <div class="index-btm">
        
          <i class="iconfont icon-riqi2"></i>&nbsp;2020-03-07&nbsp;&nbsp;
        
        
        
          <i class="iconfont icon-tag"></i>
          
            <a href="/tags/%E5%8D%B7%E7%A7%AF%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">卷积神经网络</a>&nbsp;
          
        
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      <a href="/2020/02/26/FindYourPhone/">
        <p class="h4 index-header">FindYourPhone</p>
      </a>

      <a href="/2020/02/26/FindYourPhone/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">📱 FindYourPhone利用Python爬虫查找手机号码注册过的网站✨ 特性
使用aiohttp, asyncio并发请求
查找手机的归属地, 运营商,卡类型,通信标准
易扩展, 只需在config.py中添加网站的注册/登录接口及判断条件（欢迎大家添加补充）

📜目前支持的网站（2020.2.26）



网站
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百度
√


暴风影音
√


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√


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          <i class="iconfont icon-riqi2"></i>&nbsp;2020-02-26&nbsp;&nbsp;
        
        
        
          <i class="iconfont icon-tag"></i>
          
            <a href="/tags/%E7%88%AC%E8%99%AB">爬虫</a>&nbsp;
          
        
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      <a href="/2020/02/20/wenku/">
        <p class="h4 index-header">百度文库助手</p>
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      <a href="/2020/02/20/wenku/">
        
        
          
        
        <div class="index-excerpt" >
          <div class="index-text mb-1">🔥 百度文库助手免券下载百度文库格式为 PPT, DOC, PDF(图片) 的非试看文档
✨ 特性
免券下载百度文库PPT, DOC, PDF(图片) 非试看文档
下载文件自动识别转换到相应格式（PPT PDF 保留原始图片 并生成PDF文件）
对下载的文档图片进行图像处理使字体比原始锐利，图像更鲜艳
使用PyQt5 添加GUI
增加URL校验和解决中文乱码

⚡ 环境依赖
Python3.7
requests 
BeautifulSoup
reportlab 
PIL 
PyQt5

⌛️ 食用方法
克隆仓库
git clone https://gitee.com/kali20gakki/</div>
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      <div class="index-btm">
        
          <i class="iconfont icon-riqi2"></i>&nbsp;2020-02-20&nbsp;&nbsp;
        
        
        
          <i class="iconfont icon-tag"></i>
          
            <a href="/tags/%E7%88%AC%E8%99%AB">爬虫</a>&nbsp;
          
        
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